= Offered |
= Special Topic |
= Seminar |
= Selected
Offered during current academic year.
| Description | Predictive analytics for business, accounting, and marketing with emphasis on multiple regression models, inference for regression parameters, categorical independent variables, model assumptions and diagnostics, goodness of fit and test of independence, analysis of variance for one factor and multiple factor designs, and model selection and validation. |
|---|
| Antirequisites | Biology 2244A/B, Data Science 2000A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B. |
|---|
| Prerequisites | Economics 2122A/B or Economics 2222A/B, or permission of the department. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 hours |
|---|
| Lab Hours | 2 hours | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Topics include computational linear algebra, numerical optimization, simulation, use of IDEs, and display of statistical data. |
|---|
| Antirequisites | Statistics 2864A/B, Data Science 1200A/B. |
|---|
| Prerequisites | 0.5 course from Mathematics 1229A/B, Mathematics 1600A/B, Numerical and Mathematical Methods 1411A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 2 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Decision analysis, linear programming, integer programming, dynamic programming, introduction to computer programming, statistical distributions, Markov chains, Monte Carlo simulation, queuing, discrete event simulation. Students will use a variety of tools to investigate applications including transportation networks, revenue management, and sports analytics. |
|---|
| Antirequisites | Financial Modelling 3817A/B, Statistical Sciences 4654A/B. |
|---|
| Prerequisites | 0.5 course from Analytics and Decision Sciences 2288A/B, Data Science 1200A/B, or Statistical Sciences 2864A/B; and 1.0 courses from any 1000-level courses in Applied Mathematics, Calculus, Mathematics, Numerical and Mathematical Methods, and/or Statistical Sciences. Pre-or Corequisite(s): 0.5 course from: Economics 2122A/B, Economics 2222A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2857A/B, or by permission of the School of Management, Economics, and Mathematics. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 2 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Stochastic optimization modelling, decision making under uncertainty, simulation modelling, regression and forecasting models and data mining. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Analytics and Decision Sciences 2298A/B, 0.5 from Mathematics 1229A/B or Mathematics 1600A/B; 1.0 from Economics 2122A/B, Economics 2123A/B, Economics 2222A/B, Economics 2223A/B or Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 1 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Statistical programming in a high-level language, data visualization design principles, extracting insights from data visualization, data mining and machine learning, data classification; visualization of multivariate, time-series, and hierarchical data. |
|---|
| Antirequisites | |
|---|
| Prerequisites | 0.5 course from Analytics and Decision Sciences 2288F/G or Statistics 2864A/B; and 1.0 course from Economics 2122A/B, Economics 2222A/B, Economics 2123A/B, Economics 2223A/B or Analytics and Decision Sciences 2036A/B, Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B; or by permission of the School of Management, Economics, and Mathematics. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 2 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Practical analytics and software tools explored through case analyses. Linear programming, statistical analysis, decision analysis, game theory, inventory analysis, queuing theory, simulation, Markovian decision model, and forecasting will be applied in a variety of scenarios. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Analytics and Decision Sciences 2298A/B, 0.5 from Mathematics 1229A/B or Mathematics 1600A/B; 1.0 from Economics 2122A/B, Economics 2123A/B, Economics 2222A/B, Economics 2223A/B or Analytics and Decision Sciences 2036A/B, Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 hours |
|---|
| Lab Hours | 1 hour | Tutorial Hours | |
|---|
| Notes | |
|---|
= Special Topic |
= Seminar |
= Selected
Offered during current academic year.
Sorry, there are no special topic course descriptions to display.
= Offered |
= Special Topic |
= Seminar |
= Selected
Offered during current academic year.
| Description | Predictive analytics for business, accounting, and marketing with emphasis on multiple regression models, inference for regression parameters, categorical independent variables, model assumptions and diagnostics, goodness of fit and test of independence, analysis of variance for one factor and multiple factor designs, and model selection and validation. |
|---|
| Antirequisites | Biology 2244A/B, Data Science 2000A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, Psychology 2811A/B or the former Psychology 2810, Psychology 2801F/G or the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B. |
|---|
| Prerequisites | Economics 2122A/B or Economics 2222A/B, or permission of the department. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 hours |
|---|
| Lab Hours | 2 hours | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Topics include computational linear algebra, numerical optimization, simulation, use of IDEs, and display of statistical data. |
|---|
| Antirequisites | Statistics 2864A/B, Data Science 1200A/B. |
|---|
| Prerequisites | 0.5 course from Mathematics 1229A/B, Mathematics 1600A/B, Numerical and Mathematical Methods 1411A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 2 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Decision analysis, linear programming, integer programming, dynamic programming, introduction to computer programming, statistical distributions, Markov chains, Monte Carlo simulation, queuing, discrete event simulation. Students will use a variety of tools to investigate applications including transportation networks, revenue management, and sports analytics. |
|---|
| Antirequisites | Financial Modelling 3817A/B, Statistical Sciences 4654A/B. |
|---|
| Prerequisites | 0.5 course from Analytics and Decision Sciences 2288A/B, Data Science 1200A/B, or Statistical Sciences 2864A/B; and 1.0 courses from any 1000-level courses in Applied Mathematics, Calculus, Mathematics, Numerical and Mathematical Methods, and/or Statistical Sciences. Pre-or Corequisite(s): 0.5 course from: Economics 2122A/B, Economics 2222A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2857A/B, or by permission of the School of Management, Economics, and Mathematics. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 2 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Stochastic optimization modelling, decision making under uncertainty, simulation modelling, regression and forecasting models and data mining. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Analytics and Decision Sciences 2298A/B, 0.5 from Mathematics 1229A/B or Mathematics 1600A/B; 1.0 from Economics 2122A/B, Economics 2123A/B, Economics 2222A/B, Economics 2223A/B or Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 1 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Statistical programming in a high-level language, data visualization design principles, extracting insights from data visualization, data mining and machine learning, data classification; visualization of multivariate, time-series, and hierarchical data. |
|---|
| Antirequisites | |
|---|
| Prerequisites | 0.5 course from Analytics and Decision Sciences 2288F/G or Statistics 2864A/B; and 1.0 course from Economics 2122A/B, Economics 2222A/B, Economics 2123A/B, Economics 2223A/B or Analytics and Decision Sciences 2036A/B, Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B; or by permission of the School of Management, Economics, and Mathematics. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 2 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Practical analytics and software tools explored through case analyses. Linear programming, statistical analysis, decision analysis, game theory, inventory analysis, queuing theory, simulation, Markovian decision model, and forecasting will be applied in a variety of scenarios. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Analytics and Decision Sciences 2298A/B, 0.5 from Mathematics 1229A/B or Mathematics 1600A/B; 1.0 from Economics 2122A/B, Economics 2123A/B, Economics 2222A/B, Economics 2223A/B or Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | 1 | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Practical analytics and software tools explored through case analyses. Linear programming, statistical analysis, decision analysis, game theory, inventory analysis, queuing theory, simulation, Markovian decision model, and forecasting will be applied in a variety of scenarios. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Analytics and Decision Sciences 2298A/B, 0.5 from Mathematics 1229A/B or Mathematics 1600A/B; 1.0 from Economics 2122A/B, Economics 2123A/B, Economics 2222A/B, Economics 2223A/B or Analytics and Decision Sciences 2036A/B, Statistical Sciences 2035, Statistical Sciences 2857A/B, Statistical Sciences 2858A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 hours |
|---|
| Lab Hours | 1 hour | Tutorial Hours | |
|---|
| Notes | |
|---|
| Description | Approaches for solving complex problems are learned and then applied to a group-based analytics consulting project done in collaboration with a not-for-profit, educational, private, or government partner. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Completion of Philosophy 2293A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 |
|---|
| Lab Hours | | Tutorial Hours | |
|---|
| Notes | |
|---|
There are no course outlines available for this course at this time.
| Description | Approaches for solving complex problems are learned and then applied to a group-based analytics consulting project done in collaboration with a not-for-profit, educational, private, or government partner. |
|---|
| Antirequisites | |
|---|
| Prerequisites | Completion of Philosophy 2293A/B. |
|---|
| Co-requisites | |
|---|
| Weight | 0.5 | Lecture Hours | 3 hours |
|---|
| Lab Hours | | Tutorial Hours | |
|---|
| Notes | |
|---|
There are no course outlines available for this course at this time.
